Few-Shot Learning: Few-shot learning [1]} has been studied in the context of image recognition [2]}, [3]}, reinforcement learning [4]}, and imitation learning [5]}. In the context of reinforcement and imitation learning, few-shot learning is often cast as a meta-learning problem [6]}, [4]}, [5]}, where the offline dataset of demonstrations are labeled by tasks. However, there are other means of attaining few-shot generalization that do not require meta-learning. Recently, advances in unsupervised representation learning in natural language processing [9]}, [10]} and vision [11]}, [12]} have shown how a network pre-trained with a self-supervised objective can be finetuned or adjusted with a linear probe to generalize in few-shot or even zero-shot [13]} to a downstream task. Our approach to few-shot imitation learning is loosely inspired by the generalization capabilities of networks pre-trained with unsupervised objectives. Our approach first fits a behavioral prior to an unlabeled offline dataset of demonstrations to extract skills and then fits an imitation learning policy over the previously acquired skills to generalize in few-shot to new tasks. FIST is therefore a hierarchical few-shot imitation learning algorithm.
